In the relation extraction of Chinese resources, the long Chinese sentence style is complex, the syntactic feature extraction is very difficult, and its accuracy is low. A bilingual cooperative relation extraction method based on a parallel corpus was proposed to resolve these above problems. In a Chinese and English bilingual parallel corpus, the English relation extraction classification was trained by dependency syntactic features which obtained by mature syntax analytic tools of English, the Chinese relation extraction classification was trained by n-gram feature which is suitable for Chinese, then they constituted bilingual view. Finally, based on the annotated and mapped parallel corpus, the training corpus with high reliability of both classifications were added to each other for bilingual collaborative training, and a Chinese relation extraction classification model with better performance was acquired. Experimental results on Chinese test corpus show that the proposed method improves the performance of Chinese relation extraction method based on weak supervision, its F value is increased by 3.9 percentage points.

To solve the problem that the existing IP covert timing channel need to make agreed the encoding scheme between the information and IP packet timing interval, can’t dynamic adjustment of double-blind according to the network transmission quality in the process of sending and receiving, a double-blind dynamic adjustment strategy what is used to negotiate IP covert timing channel encoding scheme is proposed. By segmenting the network environment and depending on the dynamic network environment, the strategy selected the default encoding scheme, achieved double-blind dynamic adjustment of encoding scheme between sender and receiver. In order to verify the reliability of strategy, the experimental environment of covert timing channel is constructed. The results show that the proposed method can achieve double-blind dynamic adjustment between sender and receiver.